The Path to Health Information Technology Adoption: How Far Have We Reached?

Health Information Technology (HIT) is an overarching framework that describes the management of health information across various computerized systems and the secure exchange between consumers, providers, government, and insurers. It has been viewed as a promising tool for improving the overall quality, safety and efficiency of the health delivery system (Chaudhry et al., 2006). This capstone examines the problem of urban rural divide in the process of Health IT adoption especially with regard to Electronic Health Records (EHRs). This paper also tracks the progress made during years 2009 to 2013 to the process of Electronic Health Record adoption in the United States

Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigate using this paradigm to perform $k$-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We compare the performance of this approach to classical $k$-means clustering both numerically and experimentally on IBM Q hardware. We are able to find data sets where coresets work well relative to random sampling and where QAOA could potentially outperform standard $k$-means on a coreset. However, finding data sets where both coresets and QAOA work well--which is necessary for a quantum advantage over $k$-means on the entire data set--appears to be challenging

Trustworthy Quantum Computation through Quantum Physical Unclonable Functions

Quantum computing is under rapid development, and today there are several cloud-based, quantum computers (QCs) of modest size (>100s of physical qubits). Although these QCs, along with their highly-specialized classical support infrastructure, are in limited supply, they are readily available for remote access and programming. This work shows the viability of using intrinsic quantum hardware properties for fingerprinting cloud-based QCs that exist today. We demonstrate the reliability of intrinsic fingerprinting with real QC characterization data, as well as simulated QC data, and we detail a quantum physically unclonable function (Q-PUF) scheme for secure key generation using unique fingerprint data combined with fuzzy extraction. We use fixed-frequency transmon qubits for prototyping our methods

Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers

Recent developments in engineering and algorithms have made real-world applications in quantum computing possible in the near future. Existing quantum programming languages and compilers use a quantum assembly language composed of 1- and 2-qubit (quantum bit) gates. Quantum compiler frameworks translate this quantum assembly to electric signals (called control pulses) that implement the specified computation on specific physical devices. However, there is a mismatch between the operations defined by the 1- and 2-qubit logical ISA and their underlying physical implementation, so the current practice of directly translating logical instructions into control pulses results in inefficient, high-latency programs. To address this inefficiency, we propose a universal quantum compilation methodology that aggregates multiple logical operations into larger units that manipulate up to 10 qubits at a time. Our methodology then optimizes these aggregates by (1) finding commutative intermediate operations that result in more efficient schedules and (2) creating custom control pulses optimized for the aggregate (instead of individual 1- and 2-qubit operations). Compared to the standard gate-based compilation, the proposed approach realizes a deeper vertical integration of high-level quantum software and low-level, physical quantum hardware. We evaluate our approach on important near-term quantum applications on simulations of superconducting quantum architectures. Our proposed approach provides a mean speedup of $5\times$, with a maximum of $10\times$. Because latency directly affects the feasibility of quantum computation, our results not only improve performance but also have the potential to enable quantum computation sooner than otherwise possible.Comment: 13 pages, to apper in ASPLO

Optimized Surface Code Communication in Superconducting Quantum Computers

Quantum computing (QC) is at the cusp of a revolution. Machines with 100 quantum bits (qubits) are anticipated to be operational by 2020 [googlemachine,gambetta2015building], and several-hundred-qubit machines are around the corner. Machines of this scale have the capacity to demonstrate quantum supremacy, the tipping point where QC is faster than the fastest classical alternative for a particular problem. Because error correction techniques will be central to QC and will be the most expensive component of quantum computation, choosing the lowest-overhead error correction scheme is critical to overall QC success. This paper evaluates two established quantum error correction codes---planar and double-defect surface codes---using a set of compilation, scheduling and network simulation tools. In considering scalable methods for optimizing both codes, we do so in the context of a full microarchitectural and compiler analysis. Contrary to previous predictions, we find that the simpler planar codes are sometimes more favorable for implementation on superconducting quantum computers, especially under conditions of high communication congestion.Comment: 14 pages, 9 figures, The 50th Annual IEEE/ACM International Symposium on Microarchitectur

Hardware-Conscious Optimization of the Quantum Toffoli Gate

While quantum computing holds great potential in several fields including combinatorial optimization, electronic structure calculation, and number theory, the current era of quantum computing is limited by noisy hardware. Many quantum compilation approaches, including noise-adaptive compilation and efficient qubit routing, can mitigate the effects of imperfect hardware by optimizing quantum circuits for objectives such as critical path length. Few of these approaches, however, consider quantum circuits in terms of the set of vendor-calibrated operations (i.e., native gates) available on target hardware. In this paper, we review and expand both analytical and numerical methodology for optimizing quantum circuits at this abstraction level. Additionally, we present a procedure for combining the strengths of analytical native gate-level optimization with numerical optimization. We use these methods to produce optimized implementations of the Toffoli gate, a fundamental building block of several quantum algorithms with near-term applications in quantum compilation and machine learning. This paper focuses on the IBMQ native gate set, but the methods presented are generalizable to any superconducting qubit architecture. Our analytically optimized implementation demonstrated a $18\%$ reduction in infidelity compared with the canonical implementation as benchmarked on IBM Jakarta with quantum process tomography. Our numerical methods produced implementations with six multi-qubit gates assuming the inclusion of multi-qubit cross-resonance gates in the IBMQ native gate set, a $25\%$ reduction from the canonical eight multi-qubit implementation for linearly-connected qubits. These results demonstrate the efficacy of native gate-level optimization of quantum circuits and motivate further research into this topic.Comment: 21 page

QContext: Context-Aware Decomposition for Quantum Gates

In this paper we propose QContext, a new compiler structure that incorporates context-aware and topology-aware decompositions. Because of circuit equivalence rules and resynthesis, variants of a gate-decomposition template may exist. QContext exploits the circuit information and the hardware topology to select the gate variant that increases circuit optimization opportunities. We study the basis-gate-level context-aware decomposition for Toffoli gates and the native-gate-level context-aware decomposition for CNOT gates. Our experiments show that QContext reduces the number of gates as compared with the state-of-the-art approach, Orchestrated Trios.Comment: 10 page